Image searching

ABSTRACT

As provided herein, a domain model, corresponding to a domain of an image, may be merged with a pre-trained fundamental model to generate a trained fundamental model. The trained fundamental model may comprise a feature description of the image converted into a binary code. Responsive to a user submitting a search query, a coarse image search may be performed, using a search query binary code derived from the search query, to identify a candidate group, comprising one or more images, having binary codes corresponding to the search query binary code. A fine image search may be performed on the candidate group utilizing a search query feature description derived from the search query. The fine image search may be used to rank images within the candidate group based upon a similarity between the search query feature description and feature descriptions of the one or more images within the candidate group.

RELATED APPLICATIONS

This application claims priority to and is a continuation of U.S.application Ser. No. 15/948,061, filed on Apr. 9, 2018, entitled “IMAGESEARCHING”, which claims priority to and is a continuation of U.S.application Ser. No. 14/730,476, filed on Jun. 4, 2015, entitled “IMAGESEARCHING”. U.S. application Ser. No. 15/948,061 and U.S. applicationSer. No. 14/730,476 are both incorporated herein.

BACKGROUND

A user may search for images through various search interfaces, such asa file system search, a social network search, a search engine, etc.Unfortunately, image search results for the image search may havelimited accuracy. Image search techniques may rely on low level and/orhand crafted features. Low level and/or hand crafted features mayprovide limited and low level descriptions about images, which may failto describe the images comprehensively. Additionally, image searchtechniques may utilize a linear search technique. Linear searchtechniques may have relatively slow retrieval speeds and may be resourceintensive due to searching relatively large volumes of available data(e.g., a relatively large number of images within a database that is tobe searched).

SUMMARY

In accordance with the present disclosure, one or more systems and/ormethods for image searching are provided. In an example, a featuredescription of an image may be output from a fully connected layer of apre-trained fundamental model (e.g., a convolutional neural network). Adomain of the image may be identified. A domain model, corresponding tothe domain, may be merged with the pre-trained fundamental model togenerate the trained fundamental model. The trained fundamental modelmay comprise the fully connected layer (e.g., such as a first fullyconnected layer, a second fully connected layer, and/or a latent layer).The domain model may be trained to learn visual descriptors (e.g.,visual features of content comprised in images) corresponding to thedomain to generate learned descriptors. The learned descriptors may beutilized as feature descriptions for identifying query results. Thedomain model may be merged with the pre-trained fundamental model tocreate the trained fundamental model by learning binary codes from thepre-trained fundamental model. The trained fundamental layer maycomprise a first convolutional layer, a second convolutional layer, athird convolutional layer, a fourth convolutional layer, and/or a fifthconvolutional layer. The latent layer may be utilized to convert a firstquery output, of the first fully connected layer comprising the featuredescription, into a binary code. The binary code may describe latentsemantic content of the image. The second fully connected layer may beutilized to encode semantic information of a second query output of thelatent layer. The learned features may be merged into the trainedfundamental model utilizing back-propagation.

Responsive to a user submitting a search query, a coarse image searchmay be performed using a search query binary code derived from thesearch query (e.g., such as by utilizing a second query output of thelatent layer). The coarse image search may identify a candidate group,comprising one or more images, having binary codes corresponding to thesearch query binary code.

A fine image search may be performed on the candidate group utilizing asearch query feature description derived from the search query. The fineimage search may be used to rank the one or more images within thecandidate group based upon a similarity between the search query featuredescription and feature descriptions of the one or more images withinthe candidate group. First query outputs of the first fully connectedlayer may be utilized to rank the one or more images comprised in thecandidate group. Responsive to the image comprising a rank above aranking threshold, the image may be presented to the user as a queryresult for the search query.

DESCRIPTION OF THE DRAWINGS

While the techniques presented herein may be embodied in alternativeforms, the particular embodiments illustrated in the drawings are only afew examples that are supplemental of the description provided herein.These embodiments are not to be interpreted in a limiting manner, suchas limiting the claims appended hereto.

FIG. 1 is an illustration of a scenario involving various examples ofnetworks that may connect servers and clients.

FIG. 2 is an illustration of a scenario involving an exampleconfiguration of a server that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 3 is an illustration of a scenario involving an exampleconfiguration of a client that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 4 component block diagram illustrating an example system fortraining a fundamental model.

FIG. 5 is a component block diagram illustrating an example system forimage searching, where a domain model is merged with a pre-trainedfundamental model.

FIG. 6A is a component block diagram illustrating an example system forimage searching, where a coarse image search is performed.

FIG. 6B is a component block diagram illustrating an example system forimage searching, where a fine image search is performed.

FIG. 7 is a flow chart illustrating an example method for imagesearching.

FIG. 8 is an illustration of a scenario featuring an examplenontransitory memory device in accordance with one or more of theprovisions set forth herein.

DETAILED DESCRIPTION

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments. Thisdescription is not intended as an extensive or detailed discussion ofknown concepts. Details that are known generally to those of ordinaryskill in the relevant art may have been omitted, or may be handled insummary fashion.

The following subject matter may be embodied in a variety of differentforms, such as methods, devices, components, and/or systems.Accordingly, this subject matter is not intended to be construed aslimited to any example embodiments set forth herein. Rather, exampleembodiments are provided merely to be illustrative. Such embodimentsmay, for example, take the form of hardware, software, firmware or anycombination thereof.

1. Computing Scenario

The following provides a discussion of some types of computing scenariosin which the disclosed subject matter may be utilized and/orimplemented.

1.1. Networking

FIG. 1 is an interaction diagram of a scenario 100 illustrating aservice 102 provided by a set of servers 104 to a set of client devices110 via various types of networks. The servers 104 and/or client devices110 may be capable of transmitting, receiving, processing, and/orstoring many types of signals, such as in memory as physical memorystates.

The servers 104 of the service 102 may be internally connected via alocal area network 106 (LAN), such as a wired network where networkadapters on the respective servers 104 are interconnected via cables(e.g., coaxial and/or fiber optic cabling), and may be connected invarious topologies (e.g., buses, token rings, meshes, and/or trees). Theservers 104 may be interconnected directly, or through one or more othernetworking devices, such as routers, switches, and/or repeaters. Theservers 104 may utilize a variety of physical networking protocols(e.g., Ethernet and/or Fibre Channel) and/or logical networkingprotocols (e.g., variants of an Internet Protocol (IP), a TransmissionControl Protocol (TCP), and/or a User Datagram Protocol (UDP). The localarea network 106 may include, e.g., analog telephone lines, such as atwisted wire pair, a coaxial cable, full or fractional digital linesincluding T1, T2, T3, or T4 type lines, Integrated Services DigitalNetworks (ISDNs), Digital Subscriber Lines (DSLs), wireless linksincluding satellite links, or other communication links or channels,such as may be known to those skilled in the art. The local area network106 may be organized according to one or more network architectures,such as server/client, peer-to-peer, and/or mesh architectures, and/or avariety of roles, such as administrative servers, authenticationservers, security monitor servers, data stores for objects such as filesand databases, business logic servers, time synchronization servers,and/or front-end servers providing a user-facing interface for theservice 102.

Likewise, the local area network 106 may comprise one or moresub-networks, such as may employ differing architectures, may becompliant or compatible with differing protocols and/or may interoperatewithin the local area network 106. Additionally, a variety of local areanetworks 106 may be interconnected; e.g., a router may provide a linkbetween otherwise separate and independent local area networks 106.

In the scenario 100 of FIG. 1, the local area network 106 of the service102 is connected to a wide area network 108 (WAN) that allows theservice 102 to exchange data with other services 102 and/or clientdevices 110. The wide area network 108 may encompass variouscombinations of devices with varying levels of distribution andexposure, such as a public wide-area network (e.g., the Internet) and/ora private network (e.g., a virtual private network (VPN) of adistributed enterprise).

In the scenario 100 of FIG. 1, the service 102 may be accessed via thewide area network 108 by a user 112 of one or more client devices 110,such as a portable media player (e.g., an electronic text reader, anaudio device, or a portable gaming, exercise, or navigation device); aportable communication device (e.g., a camera, a phone, a wearable or atext chatting device); a workstation; and/or a laptop form factorcomputer. The respective client devices 110 may communicate with theservice 102 via various connections to the wide area network 108. As afirst such example, one or more client devices 110 may comprise acellular communicator and may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a cellular provider. As a second such example,one or more client devices 110 may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a location such as the user's home or workplace(e.g., a WiFi network or a Bluetooth personal area network). In thismanner, the servers 104 and the client devices 110 may communicate overvarious types of networks. Other types of networks that may be accessedby the servers 104 and/or client devices 110 include mass storage, suchas network attached storage (NAS), a storage area network (SAN), orother forms of computer or machine readable media.

1.2. Server Configuration

FIG. 2 presents a schematic architecture diagram 200 of a server 104that may utilize at least a portion of the techniques provided herein.Such a server 104 may vary widely in configuration or capabilities,alone or in conjunction with other servers, in order to provide aservice such as the service 102.

The server 104 may comprise one or more processors 210 that processinstructions. The one or more processors 210 may optionally include aplurality of cores; one or more coprocessors, such as a mathematicscoprocessor or an integrated graphical processing unit (GPU); and/or oneor more layers of local cache memory. The server 104 may comprise memory202 storing various forms of applications, such as an operating system204; one or more server applications 206, such as a hypertext transportprotocol (HTTP) server, a file transfer protocol (FTP) server, or asimple mail transport protocol (SMTP) server; and/or various forms ofdata, such as a database 208 or a file system. The server 104 maycomprise a variety of peripheral components, such as a wired and/orwireless network adapter 214 connectible to a local area network and/orwide area network; one or more storage components 216, such as a harddisk drive, a solid-state storage device (SSD), a flash memory device,and/or a magnetic and/or optical disk reader.

The server 104 may comprise a mainboard featuring one or morecommunication buses 212 that interconnect the processor 210, the memory202, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol; aUniform Serial Bus (USB) protocol; and/or Small Computer SystemInterface (SCI) bus protocol. In a multibus scenario, a communicationbus 212 may interconnect the server 104 with at least one other server.Other components that may optionally be included with the server 104(though not shown in the schematic diagram 200 of FIG. 2) include adisplay; a display adapter, such as a graphical processing unit (GPU);input peripherals, such as a keyboard and/or mouse; and a flash memorydevice that may store a basic input/output system (BIOS) routine thatfacilitates booting the server 104 to a state of readiness.

The server 104 may operate in various physical enclosures, such as adesktop or tower, and/or may be integrated with a display as an“all-in-one” device. The server 104 may be mounted horizontally and/orin a cabinet or rack, and/or may simply comprise an interconnected setof components. The server 104 may comprise a dedicated and/or sharedpower supply 218 that supplies and/or regulates power for the othercomponents. The server 104 may provide power to and/or receive powerfrom another server and/or other devices. The server 104 may comprise ashared and/or dedicated climate control unit 220 that regulates climateproperties, such as temperature, humidity, and/or airflow. Many suchservers 104 may be configured and/or adapted to utilize at least aportion of the techniques presented herein.

1.3. Client Device Configuration

FIG. 3 presents a schematic architecture diagram 300 of a client device110 whereupon at least a portion of the techniques presented herein maybe implemented. Such a client device 110 may vary widely inconfiguration or capabilities, in order to provide a variety offunctionality to a user such as the user 112. The client device 110 maybe provided in a variety of form factors, such as a desktop or towerworkstation; an “all-in-one” device integrated with a display 308; alaptop, tablet, convertible tablet, or palmtop device; a wearable devicemountable in a headset, eyeglass, earpiece, and/or wristwatch, and/orintegrated with an article of clothing; and/or a component of a piece offurniture, such as a tabletop, and/or of another device, such as avehicle or residence. The client device 110 may serve the user in avariety of roles, such as a workstation, kiosk, media player, gamingdevice, and/or appliance.

The client device 110 may comprise one or more processors 310 thatprocess instructions. The one or more processors 310 may optionallyinclude a plurality of cores; one or more coprocessors, such as amathematics coprocessor or an integrated graphical processing unit(GPU); and/or one or more layers of local cache memory. The clientdevice 110 may comprise memory 301 storing various forms ofapplications, such as an operating system 303; one or more userapplications 302, such as document applications, media applications,file and/or data access applications, communication applications such asweb browsers and/or email clients, utilities, and/or games; and/ordrivers for various peripherals. The client device 110 may comprise avariety of peripheral components, such as a wired and/or wirelessnetwork adapter 306 connectible to a local area network and/or wide areanetwork; one or more output components, such as a display 308 coupledwith a display adapter (optionally including a graphical processing unit(GPU)), a sound adapter coupled with a speaker, and/or a printer; inputdevices for receiving input from the user, such as a keyboard 311, amouse, a microphone, a camera, and/or a touch-sensitive component of thedisplay 308; and/or environmental sensors, such as a global positioningsystem (GPS) receiver 319 that detects the location, velocity, and/oracceleration of the client device 110, a compass, accelerometer, and/orgyroscope that detects a physical orientation of the client device 110.Other components that may optionally be included with the client device110 (though not shown in the schematic diagram 300 of FIG. 3) includeone or more storage components, such as a hard disk drive, a solid-statestorage device (SSD), a flash memory device, and/or a magnetic and/oroptical disk reader; and/or a flash memory device that may store a basicinput/output system (BIOS) routine that facilitates booting the clientdevice 110 to a state of readiness; and a climate control unit thatregulates climate properties, such as temperature, humidity, andairflow.

The client device 110 may comprise a mainboard featuring one or morecommunication buses 312 that interconnect the processor 310, the memory301, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol;the Uniform Serial Bus (USB) protocol; and/or the Small Computer SystemInterface (SCI) bus protocol. The client device 110 may comprise adedicated and/or shared power supply 318 that supplies and/or regulatespower for other components, and/or a battery 304 that stores power foruse while the client device 110 is not connected to a power source viathe power supply 318. The client device 110 may provide power to and/orreceive power from other client devices.

In some scenarios, as a user 112 interacts with a software applicationon a client device 110 (e.g., an instant messenger and/or electronicmail application), descriptive content in the form of signals or storedphysical states within memory (e.g., an email address, instant messengeridentifier, phone number, postal address, message content, date, and/ortime) may be identified. Descriptive content may be stored, typicallyalong with contextual content. For example, the source of a phone number(e.g., a communication received from another user via an instantmessenger application) may be stored as contextual content associatedwith the phone number. Contextual content, therefore, may identifycircumstances surrounding receipt of a phone number (e.g., the date ortime that the phone number was received), and may be associated withdescriptive content. Contextual content, may, for example, be used tosubsequently search for associated descriptive content. For example, asearch for phone numbers received from specific individuals, receivedvia an instant messenger application or at a given date or time, may beinitiated. The client device 110 may include one or more servers thatmay locally serve the client device 110 and/or other client devices ofthe user 112 and/or other individuals. For example, a locally installedwebserver may provide web content in response to locally submitted webrequests. Many such client devices 110 may be configured and/or adaptedto utilize at least a portion of the techniques presented herein.

2. Presented Techniques

One or more systems and/or techniques for image searching are provided.Some traditional image searching techniques may lack an ability toprovide accurate image search results to users. The traditional imagesearching techniques may rely on low level and/or hand crafted featuresthat may fail to describe images comprehensively. Further, thetraditional image searching techniques may utilize linear searchmethods, which may be resource intensive and have slow retrieval times.

As provided herein, a feature description of an image may be output froma fully connected layer of a pre-trained fundamental model (e.g., aconvolutional neural network). The feature description may correspond toany type of image feature, such as shapes identified from edgedetection, corner detection, blob detection, ridge detection,scale-invariant feature transformation, curvature detection,thresholding, template matching, Hough transformation, and/or a varietyof other image feature types corresponding to shapes, coloration, and/orother visual properties of the image. A domain model, corresponding to adomain of the image (e.g., an object class, such as clothing, cars,children, and/or other objects that may be identified within an image),may be merged with the pre-trained fundamental model to generate atrained fundamental model. The feature description may be converted intoa binary code because the binary code may provide a relatively quick andefficient search criteria for performing a coarse image search used toidentify a candidate group of images (e.g., a number of images to searchusing a fine image search may be narrowed down to the candidate group ofimages).

Responsive to a user submitting a search query, the coarse image searchmay be performed using a search query binary code derived from thesearch query (e.g., such as by utilizing a second query output of thelatent layer). The coarse image search may identify a candidate group,comprising one or more images, having binary codes corresponding to thesearch query binary code. A fine image search may be performed on thecandidate group utilizing a search query feature description derivedfrom the search query. The fine image search may be relatively moreaccurate than the coarse image search, and may be relatively efficientbecause the fine image search is merely performed on the candidate groupof images. The fine image search may be used to rank the one or moreimages within the candidate group based upon a similarity between thesearch query feature description and feature descriptions of the one ormore images within the candidate group.

By utilizing the binary code to perform the coarse image search and thefeature description to perform the fine image search (e.g., utilizinghigh level semantic descriptions), image searching may have increasedaccuracy as compared to traditional image searching techniques.Retrieval speeds of images may be increased (e.g., up to fifty times) ascompared to traditional image search techniques based upon theutilization of the binary code. Further, retrieval speeds may beminimally affected by a size of an image database that may be searched.

FIG. 4 is an illustration of a component block diagram illustrating anexample system 400 for training a fundamental model 404 (e.g., aconvolutional neural network). The fundamental model 404 may be trainedusing images from an image database 402. In an example, the imagedatabase 402 may comprise a relatively large number of images (e.g.,about 1 million to about 2 million images, or any other number) on a1000-category image classification task or any other category imageclassification task. A pre-trained fundamental model 422 may be createdusing the image database 402 and the fundamental model 404. Thepre-trained fundamental model 422 may comprise a convolutional layer.The convolutional layer may comprise a first convolutional layer 406, asecond convolutional layer 408, a third convolutional layer 410, afourth convolutional layer 412, a fifth convolutional layer 414, or anyother number of convolutional layers.

The pre-trained fundamental model 422 may comprise a fully connectedlayer. The fully connected layer may comprise an initial fully connectedlayer 416, a first fully connected layer 418, and/or a second fullyconnected layer 420. The pre-trained fundamental model 422 may utilizemax-pooling operation and/or a rectified linear unit (ReLU). In anexample, the initial fully connected layer 416 and/or the first fullyconnected layer 418 may comprise about 4,096 nodes or any other numberof nodes. A first query output of the first fully connected layer 418 ofthe pre-trained fundamental model 422 may comprise a feature description428 of an image 424. In an example, the first query output of the firstfully connected layer 418 may be input into a 1,000 way-softmaxcomprised within the second fully connected layer 420. The second fullyconnected layer 420 may output a probability distribution over domains.

FIG. 5 is an illustration of a component block diagram illustrating anexample system 500 for merging a domain model 502 with a pre-trainedfundamental model 503 to generate a trained fundamental model 504utilizing an image searching component 530. In an example, merging thedomain model 502 may comprise utilizing a domain specific image data set(e.g., comprising 100 categories, or some other number of categories) totrain the pre-trained fundamental model 503 to generate the trainedfundamental model 504. The trained fundamental model 504 may comprise aconvolutional layer. The convolutional layer may comprise a firstconvolutional layer 506, a second convolutional layer 508, a thirdconvolutional layer 510, a fourth convolutional layer 512, a fifthconvolutional layer 514, or any other number of convolutional layers.

The trained fundamental model 504 may comprise an initial fullyconnected layer 516, a first fully connected layer 518, a latent layer520, and/or a second fully connected layer 522. The domain model 502 maybe merged by learning binary codes from the pre-trained fundamentalmodel 503. The domain model 502 may be trained to learn visualdescriptors (e.g., visual features of content comprised in images, suchas shapes) corresponding to a domain (e.g., a genre of images such asimages of houses or other objects, a product area, etc.) to generatelearned descriptors. In an example, the learned descriptors maycorrespond to the domain. A learned descriptor may comprise a featuredescription 528 of an image 524 for image retrieval. The learnedfeatures may be merged into the trained fundamental model 504 utilizingback-propagation.

The latent layer 520 may be utilized to convert a first query output, ofthe first fully connected layer 518 comprising the feature description,into a binary code 526. In an example, the first query output may beconverted into the binary code by utilizing neurons in the latent layer520. The neurons may be activated by utilizing a sigmoid function suchthat activations may be approximated into {0, 1}. The binary code 526may describe latent semantic content of the image 524 (e.g., the imagemay relate to a beach). The binary code 526 may represent a number ofhidden concepts/attributes with each hidden attribute either one of onor off. A second query output of the latent layer 520 may comprise 128nodes or some other number of nodes. In this way, the image 524 may beassociated with the binary code 526. The identification of the domainmay depend on the binary code 526 rather than on feature descriptions.

The second fully connected layer 522 (e.g., comprising 15 nodes, or someother number of nodes) may be utilized to encode semantic information ofthe second query output of the latent layer 520. Pre-trained weights(e.g., a weight given to a layer) and latent layer weights (e.g., aweight of the latent layer 520) may be randomly initialized. Astochastic gradient descent (SGD) may be performed to refine thepre-trained weights and/or the latent layer weights by maximizing amultinomial logistic regression objective. The pre-trained weightsand/or the latent layer weights may evolve into the domain model 502(e.g., a multi-layer function). The trained fundamental model 504 maylearn feature descriptions and binary codes for image retrieval.

FIGS. 6A-6B illustrate a component block diagram illustrating an examplesystem 600 for searching for an image using an image searching component630. A user, of a client device 602, may enter a search query 606 (e.g.,an image query) into a search website 604. The search query 606 may beprovided to the image searching component 630. The image searchingcomponent 630 may derive a search query binary code 608 and/or a searchquery feature description 609 from the search query 606. In an example,the search query binary code 608 may be extracted from a second queryoutput of a latent layer. The search query binary code 608 may beutilized to perform a coarse image search 611. A candidate group 610,comprising one or more images (e.g., an image 612, a second image 618,and/or other images not illustrated), having binary codes correspondingto the search query binary code 608 may be identified. The candidategroup 610 may comprise images with a Hamming distance less than athreshold. In an example, the candidate group 610 may comprise the image612, comprising a binary code 614 and/or a feature description 616, andthe second image 618, comprising a second binary code 620 and/or asecond feature description 622. In an example, the binary code 614and/or the second binary code 620 may correspond to the search querybinary code 608 (e.g., binary codes that exceed a threshold similarity).

FIG. 6B illustrates the image searching component 630 performing a fineimage search 624. The fine image search 624 may be performed on thecandidate group 610 (e.g., the image 612, the second image 618, etc.)utilizing the search query feature description 609 derived from thesearch query 606. A first query output of a first fully connected layermay be used to rank 626 the one or more images within the candidategroup 610. In an example, the ranking 626 may be based upon a similaritybetween the search query feature description 609 and featuredescriptions of the one or more images within the candidate group 610.In an example, the similarity may be determined based upon a Euclideandistance between feature descriptions of the one or more images.Responsive to the image 612 compromising a ranking above a rankingthreshold, the image 612 may presented to the user as a query result 628for the search query 606. In an example, responsive to the second image618 compromising a ranking below the ranking threshold, the second image618 may not be presented to the user as the query result 628.

An embodiment of image searching is illustrated by an example method 700of FIG. 7. At 702, the method 700 starts. At 704, a fundamental modelmay be trained using an image database to create a pre-trainedfundamental model. In an example, the pre-trained fundamental model maycomprise a convolutional layer and a fully connected layer. At 706, afeature description of an image may be output from the fully connectedlayer of the pre-trained fundamental model. At 708, a domain of theimage may be identified. At 710, a domain model, corresponding to thedomain, may be merged with the pre-trained fundamental model to generatea trained fundamental model. At 712, the feature description may beconverted into a binary code. At 714, responsive to a user submitting asearch query, a coarse image search may be performed using a searchquery binary code. The search query binary code may be derived from thesearch query. The coarse image search may be used to identify acandidate group. The candidate group may comprise one or more imageshaving binary codes corresponding to the search query binary code. At716, a fine image search may be performed on the candidate grouputilizing a search query feature description derived from the searchquery. The fine image search may rank the one or more images within thecandidate group based upon a similarity between the search query featuredescription and feature descriptions of the one or more images withinthe candidate group. At 718, responsive to the image comprising a rankabove a ranking threshold, the image may be presented to the user as aquery result for the search query. At 720, the method 700 ends.

FIG. 8 is an illustration of a scenario 800 involving an examplenontransitory memory device 802. The nontransitory memory device 802 maycomprise instructions that when executed perform at least some of theprovisions herein. The nontransitory memory device may comprise a memorysemiconductor (e.g., a semiconductor utilizing static random accessmemory (SRAM), dynamic random access memory (DRAM), and/or synchronousdynamic random access memory (SDRAM) technologies), a platter of a harddisk drive, a flash memory device, or a magnetic or optical disc (suchas a CD, DVD, or floppy disk). The example nontransitory memory device802 stores computer-readable data 804 that, when subjected to reading806 by a reader 810 of a device 808 (e.g., a read head of a hard diskdrive, or a read operation invoked on a solid-state storage device),express processor-executable instructions 812. In some embodiments, theprocessor-executable instructions, when executed on a processor 816 ofthe device 808, are configured to perform a method, such as at leastsome of the example method 700 of FIG. 7, for example. In someembodiments, the processor-executable instructions, when executed on theprocessor 816 of the device 808, are configured to implement a system,such as at least some of the example system 400 of FIG. 4, at least someof the example system 500 of FIG. 5, and/or at least some of the examplesystem 600 of FIGS. 6A-6B, for example.

3. Usage of Terms

As used in this application, “component,” “module,” “system”,“interface”, and/or the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Unless specified otherwise, “first,” “second,” and/or the like are notintended to imply a temporal aspect, a spatial aspect, an ordering, etc.Rather, such terms are merely used as identifiers, names, etc. forfeatures, elements, items, etc. For example, a first object and a secondobject generally correspond to object A and object B or two different ortwo identical objects or the same object.

Moreover, “example” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused herein, “or” is intended to mean an inclusive “or” rather than anexclusive “or”. In addition, “a” and “an” as used in this applicationare generally be construed to mean “one or more” unless specifiedotherwise or clear from context to be directed to a singular form. Also,at least one of A and B and/or the like generally means A or B or both Aand B. Furthermore, to the extent that “includes”, “having”, “has”,“with”, and/or variants thereof are used in either the detaileddescription or the claims, such terms are intended to be inclusive in amanner similar to the term “comprising”.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing at least some of the claims.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

Various operations of embodiments are provided herein. In an embodiment,one or more of the operations described may constitute computer readableinstructions stored on one or more computer readable media, which ifexecuted by a computing device, will cause the computing device toperform the operations described. The order in which some or all of theoperations are described should not be construed as to imply that theseoperations are necessarily order dependent. Alternative ordering will beappreciated by one skilled in the art having the benefit of thisdescription. Further, it will be understood that not all operations arenecessarily present in each embodiment provided herein. Also, it will beunderstood that not all operations are necessary in some embodiments.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A system for image searching, comprising: aprocessor; and memory comprising processor-executable instructions thatwhen executed by the processor cause implementation of an imagesearching component configured to: convert a feature description of animage into a binary code using a trained fundamental model; responsiveto a user submitting a search query, perform a coarse image search usinga search query binary code derived from the search query to identify acandidate group, comprising one or more images; and perform a fine imagesearch on the candidate group utilizing a search query featuredescription derived from the search query to rank the one or more imageswithin the candidate group, the candidate group comprising the imagehaving the feature description.
 2. The system of claim 1, the imagesearching component configured to: responsive to the image comprising aranking above a ranking threshold, present the image to the user as aquery result for the search query.
 3. The system of claim 1, the imagesearching component configured to: create the trained fundamental modelto comprise a first convolutional layer, a second convolutional layer, athird convolutional layer, a fourth convolutional layer, and a fifthconvolutional layer.
 4. The system of claim 1, the image searchingcomponent configured to: create the trained fundamental model tocomprise a first fully connected layer, a second fully connected layer,and a latent layer.
 5. The system of claim 4, the image searchingcomponent configured to: utilize the latent layer to convert a firstquery output, of the first fully connected layer comprising the featuredescription, into the binary code describing latent semantic content ofthe image.
 6. The system of claim 5, the image searching componentconfigured to: utilize the second fully connected layer to encodesemantic information of a second query output of the latent layer. 7.The system of claim 5, the image searching component configured to:input the search query into the coarse image search; and generate, usingthe coarse image search, the search query binary code by utilizing asecond query output of the latent layer.
 8. The system of claim 5, theimage searching component configured to: input the candidate group intothe fine image search; and utilize one or more first query outputs ofthe first fully connected layer, using the fine image search, to rankthe one or more images comprised in the candidate group.
 9. The systemof claim 1, the trained fundamental model comprising a convolutionalneural network.
 10. The system of claim 1, the image searching componentconfigured to: transfer learned descriptors to the trained fundamentalmodel, from a pre-trained fundamental model, utilizing back-propagation.11. The system of claim 1, the image searching component configured to:train the trained fundamental model to learn binary codes from apre-trained fundamental model and a domain model.
 12. A method of imagesearching comprising: converting a feature description of an image intoa binary code; responsive to a user submitting a search query,performing a coarse image search using a search query binary codederived from the search query to identify a candidate group, comprisingone or more images; and performing a fine image search on the candidategroup utilizing a search query feature description derived from thesearch query to rank the one or more images within the candidate group,the candidate group comprising the image having the feature description.13. The method of claim 12, comprising: responsive to the imagecomprising a rank above a ranking threshold, presenting the image to theuser as a query result for the search query.
 14. The method of claim 12,comprising: creating a trained fundamental model to comprise a firstfully connected layer, a second fully connected layer, and a latentlayer, the converting performed using the trained fundamental model. 15.The method of claim 14, comprising: utilizing the latent layer toconvert a first query output, of the first fully connected layercomprising the feature description, into the binary code describing alatent semantic content of the image; and utilizing the second fullyconnected layer to encode semantic information of a second query outputof the latent layer.
 16. The method of claim 14, the performing a courseimage search comprising: inputting the search query into the coarseimage search; and generating, using the coarse image search, the searchquery binary code by utilizing a second query output of the latentlayer.
 17. The method of claim 14, the performing a fine image searchcomprising: inputting the candidate group into the fine image search;and utilizing first query outputs of the first fully connected layer,using the fine image search, to rank the one or more images comprised inthe candidate group.
 18. The method of claim 12, the convertingperformed using a convolutional neural network.
 19. A system for imagesearching, comprising: a processor; and memory comprisingprocessor-executable instructions that when executed by the processorcause implementation of an image searching component configured to:convert a feature description of an image into a binary code using atrained fundamental model; identify a candidate group, comprising one ormore images, having binary codes corresponding to a second binary code;and perform a fine image search on the candidate group utilizing asecond feature description to rank the one or more images within thecandidate group, the candidate group comprising the image having thefeature description.
 20. The system of claim 19, the image searchingcomponent configured to: responsive to the image comprising a rank abovea ranking threshold, present the image to a user.